1. Technical Field of the Invention
The present invention relates to evolutionary neural networks, a method of generating evolutionary neural networks and a computer program product.
2. Description of Related Art
Artificial neural networks are computational models of the nervous systems. Natural organisms, however, do not posses only nervous systems but also genetic information stored in their cells. This genetic information is called genotype. The information specified in the genotype determines, inter alia, the general aspects of the nervous system, whereas the individual aspects (phenotype) of the nervous system is derived from the genotype through a process called development. Similarly to natural organisms, artificial neural networks can also be evolved by using evolutionary algorithms.
To evolve neural networks, it should be decided how to encode aspects of the neural network in the genotype in a manner suitable for the application of genetic operators that are used to randomly modify the information stored in the genotype when a new individual (i.e. a new neural network, in this case) with a specific phenotype is reproduced. One possible way to code genetic information is direct encoding. In a direct encoding scheme, there is a one-to-one correspondence between the genes and the phenotypical characters subjected to the evolutionary process. This kind of encoding is described, for example, in Miller et al., “Designing neural networks using genetic algorithms ”(Proc. of the Third International Conference on Genetic Algorithms, San Mateo, Calif., USA, 1989, pp. 379-384). Another most promising way to encode genetic information is indirect encoding, which allows to develop more flexible and scalable (open-ended) encoding schemes. These encoding schemes include, for example, the marker-based encoding described in Fullmer at al., “Using marker-based genetic encoding of neural networks to evolve finite-state behaviour” (Proc. of the First European Conference on Artificial Life, MIT Press, Cambridge, Mass., USA, 1992, pp. 255-262), and the tree structure encoding described in Zhang et al., “Genetic programming of minimal neural nets using Occam's razor” (Proc. of the Fifth International Conference on Genetic Algorithms, 1993, pp. 342-349).